Understanding key AI concepts
You may have come across many terms when you started exploring the topic of AI. We will demystify AI and only present those concepts that are most relevant to you as an RPA developer. Please note that you may come across other material with slightly different definitions based on a different context.
Differentiating between artificial intelligence, machine learning, and deep learning
AI, ML, and deep learning (DL) are related but not the same. The following figure illustrates the hierarchy of these types of learning:
Figure 1.1 – AI, ML, and DL
- AI: This is equivalent to giving a machine or a robot the ability to think. It encompasses ML and DL.
- ML: This refers to how a machine or a robot learns to think through algorithms without explicit programming. ML is a subset of AI.
- DL: This refers to how an ML algorithm leverages artificial neural networks to mimic learning. DL is a subset of ML.
Next, we will look at three key considerations when choosing between ML and DL. They are listed here:
- Data requirement and availability
- Computational power
- Training time
The following figure shows a comparison of ML and DL:
Figure 1.2 – Comparison of ML and DL
In ML, the features of the studied subjects are fed into the algorithms for the machine to learn. We can think of features as us giving hints to the algorithm. This step allows for a smaller dataset, lower computational power, and less training time.
In DL, features are determined by artificial neural networks. It needs to work much harder to figure out the features and patterns to learn. As a result, it requires a large amount of data, high computational power, and a long training time.
Although DL is valuable, it is beyond the reach of most businesses to develop DL models to solve their business problems. Fortunately, many DL models have been pre-trained by companies with the time and budget to make them accessible to a large user base.
The implication of this option means that your role as an RPA developer is not to create these models. You, as the RPA developer, are the trainer of these models. It is important to understand the role of training in AI.
Appreciating the relevance of supervised learning, unsupervised learning, and reinforcement learning in AI
As we learned in the previous section, AI is about training a machine or a robot to think. Just like a human being, a robot needs to learn. There are three different types of learning for a robot.
The following figure gives some analogies for supervised learning, unsupervised learning, and reinforced learning:
Figure 1.3 – Supervised learning, unsupervised learning, and reinforcement learning analogies
The following list explains the various analogies:
- Supervised learning: This is based on past data, and the trainer specifies the inputs to predict future outcomes. This type of training is analogous to an instructor-led training course. It requires the trainer to supervise the student or the model to achieve the desired learning outcome. Classification and regression are types of supervised learning methods:
- Classification refers to the process of categorizing a given set of data into classes. For example, a set of pictures of different animals are fed into the ML model. Each picture is labeled with an animal name. The ML model is trained to identify animals from an image.
- Regression helps in the prediction of a continuous variable. For example, a profit prediction ML model is an example of a regression model. Training data consisting of R and D, marketing, and administrative spending, geographic location, and profit is fed into the model. The ML model predicts the profit.
- Unsupervised learning: This relies on an algorithm to identify unknown patterns from data. This type of training is analogous to a self-study course. It requires the students or the model to synthesize the information to achieve the desired learning outcome. Clustering is a type of unsupervised learning method:
- Clustering refers to the method used to find similarity and relationship patterns among training datasets, and then cluster those datasets into groups with similarities based on features. For example, the clustering technique is commonly used in market segmentation. The ML model looks at features such as sex, age, race, and geographic location to group customer groups into segments to better understand their buying habits.
- Reinforced learning: This uses a reward-and-punishment system to learn. There is no training data or trainer. The algorithm is improved over time based on feedback or reward and punishment. This type of training is analogous to on-the-job training. If the worker is doing the job well, the worker gains a pay raise or promotion. If the worker is performing poorly, the worker receives no raise or promotion. This is commonly used when no data or specific expertise is available.
Practical tips
AI platform providers have a mission to make AI accessible. Part of that mission is striving to develop product features to overcome the complex concepts of AI. Specifically, these are some notable democratization efforts in AI:
- Increased availability of pre-trained models to accelerate the time to result
- Simplification of the technical complexity of the ML training life cycle
We presented the key AI concepts in an easily digestible format. This overview prepares you to pick up an AI platform such as UiPath quickly. You will build, deploy, and maintain your first AI+RPA use cases in no time. You no longer need to spend years mastering AI to build a model from scratch. Instead, you are the trainer of the robots, teaching different skills that they need to master. Most importantly, you have tools that do the most complex tasks for you.
Now that you have a good understanding of the key AI concepts, let's explore cognitive automation, which is the combination of AI and RPA.
